How to compute confidence on logistic regression model? 

Confidence intervals for logistic regression models can be computed using various methods. One approach is model-averaging, which accounts for the uncertainty associated with model selection . Another method is the use of fixed size confidence regions, which estimate the fixed and random effects parameters of the mixed effects logistic regression model . In high dimensional data, new methods have been proposed that estimate and construct confidence regions for regression parameters using sparsity assumptions . Additionally, the maximum likelihood and related estimators can be used in clustered logistic joinpoint models to produce confidence bounds . These methods have been evaluated and compared using simulation studies and real data examples.

Towards Using Model Averaging To Construct Confidence Intervals In Logistic Regression Models

The paper proposes and evaluates new confidence interval estimation approaches for logistic regression models using model-averaging and the score test. The methods have been implemented in the 'mataci' R package.

Honest confidence regions for a regression parameter in logistic regression with a large number of controls

The paper proposes new methods for estimating and constructing confidence regions for a regression parameter in logistic regression models with high dimensional data. The methods allow for estimating the parameter at the root-n rate using sparsity assumptions. The resulting confidence regions are "honest" and robust with respect to model selection mistakes. However, the specific computation details for confidence intervals are not provided in the paper.

Fixed Size Confidence Regions for the Parameters of the Mixed Effects Logistic Regression Model

The paper provides two sequential procedures for estimating fixed and random effects parameters in a mixed effects logistic regression model, but does not explicitly mention how to compute confidence intervals for these parameters.

Confidence Estimation via the Parametric Bootstrap in Logistic Joinpoint Regression.

The paper discusses the use of the parametric bootstrap method to compute confidence bounds in a logistic joinpoint regression model.